• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.256-261
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• 2018

There are three types of noise generated inside the vehicle: BSR (Buzz, Squeak, Rattle). In this paper, we propose a classifier that automatically classifies automotive BSR noise by using features extracted from deep convolutional neural networks. In the preprocessing process, the features of above three noises are represented as noise-map using STFT (Short-time Fourier Transform) algorithm. In order to cope with the problem that the position of the actual noise is unknown in the part of the generated noise map, the noise map is divided using the sliding window method. In this paper, internal parameter of the deep convolutional neural networks is visualized using the t-SNE (t-Stochastic Neighbor Embedding) algorithm, and the misclassified data is analyzed in a qualitative way. In order to analyze the classified data, the similarity of the noise type was quantified by SSIM (Structural Similarity Index) value, and it was found that the retractor tremble sound is most similar to the normal travel sound. The classifier of the proposed method compared with other classifiers of machine learning method recorded the highest classification accuracy (99.15 %).

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.9 s.339
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• pp.73-80
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• 2005

The Turbo decoders based on Log-MAP decoding algorithm inherently requires large amount of memory and intensive complexity of hardware due to iterative decoding, despite of excellent decoding efficiency. To decrease the large amount of memory and reduce hardware complexity, the result of previous research. And this paper design the Turbo decoder applicable to the 3G W-CDMA systems. Through the result of previous research, we decided 5-bits for the received data 6-bits for a priori information, and 7-bits for the quantization state metrics. The error correction term for $MAX^{*}$ operation which is the main function of Log-MAP decoding algorithm is implemented with very small hardware overhead. The proposed Turbo decoder is synthesized in $0.35\mu$m Hynix CMOS technology. The synthesized result for the Turbo decoder shows that it supports a maximum 9Mbps data rate, and a BER of $10^{-6}$ is achieved(Eb/No=1.0dB, 5 iterations, and the interleaver size $\geq$ 2000).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.4
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• pp.391-397
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• 2003

This paper is about the digital image decimation algorithm which generates a value of decimated element by an average of a target pixel value and a value of neighbor intelligible element to adaptively reflect the merits of ZOD method and FOD method on the decimated image. First, a target pixel located at the center of sliding window is selected, then the gradient amplitudes of its right neighbor pixel and its lower neighbor pixel are calculated using first order derivative operator respectively. Secondly, each gradient amplitude is divided by the summation result of two gradient amplitudes to generate each intelligible weight. Next, a value of neighbor intelligible element is obtained by adding a value of the right neighbor pixel times its intelligible weight to a value of the lower neighbor pixel times its intelligible weight. The decimated image can be acquired by applying the process repetitively to all pixels in input image which generates the value of decimated element by calculating the average of the target pixel value and the value of neighbor intelligible element.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10A
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• pp.965-973
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• 2007

We analyzed that the loss of data in TCP protocol based wireless networks caused by overlapped responses in bi-directional nodes that were resulted in out of the data sequence. This loss can be prevented by using revised TCP rate control algorithm and the performance of throughput can also be improved. The rate control algorithm is applied when the congestion happens between nodes while traffic packets are retransmitting in TCP bandwidth. In addition to applying the rate control algorithm, we determine the number of system clients in bandwidth and the average of pausing time between transmitting serial files to produce a competitive level so that an efficient performance of rapid retransmitting for the loss of multi-packets. This paper discusses the improvement of congestion control in that the decrease of the loss, firstly, as ensuring an efficient connection rate and, secondly, as using sliding window flow control.

• Journal of KIISE:Databases
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• v.31 no.2
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• pp.140-149
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• 2004

Data broadcast has received much attention as a very efficient method for disseminating data items in mobile environment with large number of mobile clients. In this approach, a database server periodically and continuously broadcasts data items through wireless channels and clients perform read-only transactions by accessing necessary data items from the air. While broadcasting, the server must also process update transactions on the database, which raises an obstacle for client's accessing consistent data. In this research, we propose a new algorithm SCDSC(Serialization Checking with DirtySet on Commit) which is an alternative for solving the concurrency control problem efficiently. The SCDSC is a kind of optimistic concurrency control in that a client checks the consistency of data using a DirtySet as a part of data broadcast when it commits its transaction. In each broadcast cycle, the server updates and disseminates the DirtySet with newly changed data items for last few cycles in the sliding window approach. We perform an analysis and a simulation study to evaluate the performance of our SCDSC algorithm in terms of data consistency and data currency.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.59-69
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• 2007

In the video with a various environment, background modeling is important for extraction and recognition the moving object. For this object recognition, many methods of the background modeling are proposed in a process of preprocess. Among these there is a Kumar method which represents the Queue-based background modeling. Because this has a fixed period of updating examination of the frame, there is a limit for various system. This paper use a background modeling based on the queue. We propose the method that major parameters are decided as adaptive by background model. They are the queue size of the sliding window, the sire of grouping by the brightness of the visual and the period of updating examination of the frame. In order to determine the factors, in every process, RCO (Ratio of Correct Object), REO (Ratio of Error Object) and UR (Update Ratio) are considered to be the standard of evaluation. The proposed method can improve the existing techniques of the background modeling which is unfit for the real-time processing and recognize the object more efficient.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.4
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• pp.206-211
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• 2005

We describe the design for a correlator and an access code generator in bluetooth system. These are used for a connection setting, a packet decision and a clock synchronization between Bluetooth units. The correlator consists of two blocks; carry save adder based on Wallace tree and threshold-value decision block. It determines on an useful packet and clock-synchronization for input signal of 1.0Mbps through the sliding-window correlating. The access-code generator also consists of two blocks; BCH(Bose-Chadhuri-Hocquenghem) cyclic encoder and control block. It generates the access-codes according to four steps' generation process based on Bluetooth standard. In order to solve synchronization problem, we make use of any memory as a pseudo random sequence. The proposed correlator and access-code generator were coded with VHDL. An FPGA Implementation of these modules and the simulation results are proved by Xilinx chip. The critical delay and correlative margin based on synthesis show the 4.689ns and the allowable correlation-error up to 7-bit.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.535-544
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• 2020

In synthetic aperture radar (SAR) based ship detection application, false alarms frequently occur due to various noises caused by the radar imaging process. Among them, radio frequency interference (RFI) and azimuth smearing produce substantial false alarms; the latter also yields longer length estimation of ships than the true length. These two noises are prominent at cross-polarization and relatively weak at co-polarization. However, in general, the cross-polarization data are suitable for ship detection, because the radar backscatter from background sea surface is much less in comparison with the co-polarization backscatter, i.e., higher ship-sea image contrast. In order to improve the ship detection accuracy further, the RFI and azimuth smearing need to be mitigated. In the present letter, Sentinel-1 VV- and VH-polarization intensity data are used to show a novel technique of removing these noises. In this method, median image intensities of noises and background sea surface are calculated to yield arithmetic tendency. A band-math formula is then designed to replace the intensities of noise pixels in VH-polarization with adjusted VV-polarization intensity pixels that are less affected by the noises. To verify the proposed method, the adaptive threshold method (ATM) with a sliding window was used for ship detection, and the results showed that the 74.39% of RFI false alarms are removed and 92.27% false alarms of azimuth smearing are removed.

• Physical Therapy Korea
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• v.14 no.2
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• pp.53-60
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• 2007

The objective of this study was to discover the effects of the short-term home visiting physical therapy program involving patients with some chronic brain disorders at Gimhae City, Kyongnam. Recovery of activities of daily living (ADL) is a very important factor of rehabilitative procedures, and Functional Independence Measure (FIM) is a useful standard of evaluation for it. The FIM is widely used in brain disorder research because it measures real functional activities of daily living. We applied the physical therapy exercise program twice per week (10~15 times repeatedly); a warm-up and cool down exercise performed every ten minutes by active & active-assistive ROM and stretching exercises. Main exercises were composed of getting up & laying down in bed, standing training, walking exercise in the room, and window or wall sliding exercise using affected upper limbs for a total duration of 30 minutes. We collected the data from 20 patients with chronic brain disorders at his/her home and analyzed by means of SPSS/PC+ program (Ver. 10.0). After the six week long physical therapy exercise program, the average was $56.10{\pm}22.59$ point compared with initial $50.55{\pm}19.12$ point by FIM, improved functional ADL ability about 5.55 point, and these changed scores were statistically significant (p=.000). We also studied another factor regarding patient's satisfaction. The majority of subjects (10 people) rated the program with the maximum score of ten points (50.0%), and three people rated it a seven point program (15.0%), the other two subjects gave a rating of nine and eight points (10.0%). Because the program was effective at improving the physical ADL ability and satisfaction of each subject, we suggest continual development and implementation of a home visiting physical therapy program. Further study should involve a longer period of observation with a larger population that is involved in an individually designed home physical therapy program.

• Journal of IKEEE
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• v.21 no.4
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• pp.358-367
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• 2017

Advanced Driver Assistance Systems(ADAS), such as Front Collision Warning System (FCWS) are currently being developed. FCWS require high processing speed because it must operate in real time while driving. In addition, a low-power system is required to operate in an automobile embedded system. In this paper, FCWS is implemented in CPU-FPGA architecture in embedded system to enable real-time processing. The lane detection enabled the use of the Inverse Transform Perspective (IPM) and sliding window methods to operate at fast speed. To detect the vehicle, a Convolutional Neural Network (CNN) with high recognition rate and accelerated by parallel processing in FPGA is used. The proposed architecture was verified using Intel FPGA Cyclone V SoC(System on Chip) with ARM-Core A9 which operates in low power and on-board FPGA. The performance of FCWS in HD resolution is 44FPS, which is real time, and energy efficiency is about 3.33 times higher than that of high performance PC enviroment.